Electric submersible pump event detection

ABSTRACT

A method for monitoring operation of an electric submersible pump. The method includes receiving data signals indicating values for a plurality of parameters regarding operation of the electric submersible pumping system; establishing, for at least some of the plurality of parameters, an associated reference signal; and detecting a deviation of one of the parameters from the reference signal associated with that parameter. As a result of the deviation having a rate of change below a predetermined threshold, the method includes updating the value of the reference signal to reflect the deviation. As a result of the deviation having a rate of change above the predetermined threshold, the method includes detecting an event and generating an indication of the event. The indication of the event further depends on a type of the parameter and a direction of the deviation from the reference signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional ApplicationNo. 62/089,480 filed Dec. 9, 2014, entitled “Electric Submersible PumpEvent Detection,” which is incorporated herein by reference in itsentirety for all purposes.

BACKGROUND

Electric submersible pumps (ESPs) may be deployed for any of a varietyof pumping purposes, and often comprise a submersible pump powered by asubmersible motor which is protected by a motor protector. For example,where a substance (e.g., hydrocarbons in an earthen formation) does notreadily flow responsive to existing natural forces, an ESP may beimplemented to artificially lift the substance. If an ESP fails duringoperation, the ESP must be removed from the pumping environment andreplaced or repaired, either of which results in a significant cost toan operator.

In various applications, sensors or other detectors are used to detectpumping system failure and to output a warning regarding pumping systemfailure. Additionally, some well-related pumping applications employsensors to monitor aspects of the pumping system operation, andsurveillance engineers are employed to monitor the data and to makedecisions regarding pumping system operation based on that data.However, such techniques may not address pumping system issues soonenough and may be subject to errors.

SUMMARY

Embodiments of the present disclosure are directed to a method formonitoring operation of an electric submersible pump. The methodincludes receiving data signals indicating values for a plurality ofparameters regarding operation of the electric submersible pumpingsystem; establishing, for at least some of the plurality of parameters,an associated reference signal; and detecting a deviation of one of theparameters from the reference signal associated with that parameter. Asa result of the deviation having a rate of change below a predeterminedthreshold, the method includes updating the value of the referencesignal to reflect the deviation. As a result of the deviation having arate of change above the predetermined threshold, the method includesdetecting an event and generating an indication of the event. Theindication of the event depends on a type of the parameter and adirection of the deviation from the reference signal.

Other embodiments of the present disclosure are directed to a system formonitoring operation of an electric submersible pump. The systemincludes a plurality of sensors to generate data indicative of aplurality of observable parameters regarding operation of the electricsubmersible pumping system and a processor coupled to the sensors. Theprocessor is configured to receive the data from the sensors; establish,for at least some of the plurality of parameters, an associatedreference signal; detect a deviation of one of the parameters from thereference signal associated with that parameter; as a result of thedeviation having a rate of change below a predetermined threshold,update the value of the reference signal to reflect the deviation; andas a result of the deviation having a rate of change above thepredetermined threshold, generate an indication of an event. Theindication of the event depends on a type of the parameter and adirection of the deviation from the reference signal.

Still other embodiments of the present disclosure are directed to anon-transitory computer-readable medium containing instructions that,when executed by a processor, cause the processor to receive dataindicative of a plurality of observable parameters from one or moresensors; establish, for at least some of the plurality of parameters, anassociated reference signal; detect a deviation of one of the parametersfrom the reference signal associated with that parameter; as a result ofthe deviation having a rate of change below a predetermined threshold,update the value of the reference signal to reflect the deviation; andas a result of the deviation having a rate of change above thepredetermined threshold, generate an indication of an event. Theindication of the event depends on a type of the parameter and adirection of the deviation from the reference signal.

The foregoing has outlined rather broadly a selection of features of thedisclosure such that the detailed description of the disclosure thatfollows may be better understood. This summary is not intended toidentify key or essential features of the claimed subject matter, nor isit intended to be used as an aid in limiting the scope of the claimedsubject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are described with reference to thefollowing figures:

FIG. 1 is an illustration of an example of an electric submersiblepumping system deployed in a wellbore, according to an embodiment of thedisclosure;

FIG. 2 is an illustration of an example of an electric submersiblepumping system experiencing an event, in particular a low flow event,according to an embodiment of the disclosure;

FIG. 3 is a graphical illustration of an example of an event, inparticular a low flow due to insufficient lift event, according to anembodiment of the disclosure;

FIG. 4 is an illustration of an example of an automatic event detectionalgorithm which may be used to automatically monitor data and detect aspecific event or events, according to an embodiment of the disclosure;

FIG. 5 is a graphical illustration of an example of an event, inparticular a low flow due to gas interference event, according to anembodiment of the disclosure; and

FIG. 6 is an illustration of a flow chart of a method for monitoringoperation of an electric submersible pumping system, according to anembodiment of the disclosure.

NOTATION AND NOMENCLATURE

Various terms are used herein, which are defined as follows:

Channel: There may be multiple sensors placed at different locations asillustrated in FIG. 1. The set of measurements received from the samesensor is called a channel.

Amps is a measurement of drive current. This measurement represents theload of the pump.

T_(m) refers to motor temperature. This measurement represents theinside motor temperature.

P_(i) refers to intake pressure. This measurement represents thepressure at the intake of the pump.

P_(d) refers to discharge pressure. This measurement represents thepressure at the discharge of the pump.

P_(i (static)) refers to a static pressure at the intake of the pump, orthe pressure to which intake pressure P_(i) reverts to when the pump isturned off.

f refers to frequency and, more specifically drive frequency in thecontext of the present disclosure. This measurement represents the speedof the motor (with minor reduction from electrical-to-mechanicalrotation conversion or “slip”). f influences both P_(d) and P_(i); P_(d)increases proportionally with an increase in f while P_(i) decreasesproportionally with the increase in f.

t refers to time.

Q refers to fluid flow rate through the pump. This quantity indicateswhether there is a low/no flow condition exists at the pump. Q dependson both the difference in reservoir and intake pressures (i.e.,P_(i (static))−P_(i)) and the difference in discharge and intakepressures (i.e., P_(d)−P_(i)).

ΔP refers to the difference in discharge and intake pressures (i.e.,P_(d)−P_(i)).

CLa refers to current leakage active. This value is measured when thepump is off and thus represents the condition of cable insulation and/orESP system insulation. The leakage current is indicative of the healthof the ESP motor cables and thus can be used to monitor ground faults,which result in inferior data quality when present.

Cf refers to a full calibration current. This value is mapped to theupper bounds of the gauge measurement capability.

Cz refers to a zero calibration current. This value is mapped to thelower bounds of the gauge measurement capability.

T_(i) refers to ambient temperature at the intake of the pump.

WHP refers to wellhead pressure. This value is measured on the surfaceand represents the pressure before the choke. The WHP value may behighly proportional to choke position. For example, if an operatorcloses the choke, WHP increases and vice versa.

CDP refers to choke downstream pressure. This channel is measured on thesurface and represents the pressure after the choke. When CDP iscombined with WHP, the combination may represent the flow at thesurface.

WHT refers to wellhead temperature. WHT is measured at the wellhead andaffected by the fluid temperature inside the tubing and the ambienttemperature outside the tubing.

Choke position refers to a measure of the choke, and is commonly givenas an aperture percentage.

DETAILED DESCRIPTION

One or more embodiments of the present disclosure are described below.These embodiments are merely examples of the presently disclosedtechniques. Additionally, in an effort to provide a concise descriptionof these embodiments, all features of an actual implementation may notbe described in the specification. It should be appreciated that in thedevelopment of any such implementation, as in any engineering or designproject, numerous implementation-specific decisions are made to achievethe developers' specific goals, such as compliance with system-relatedand business-related constraints, which may vary from one implementationto another. Moreover, it should be appreciated that such developmentefforts might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. The embodiments discussed beloware intended to be examples that are illustrative in nature and shouldnot be construed to mean that the specific embodiments described hereinare necessarily preferential in nature. Additionally, it should beunderstood that references to “one embodiment” or “an embodiment” withinthe present disclosure are not to be interpreted as excluding theexistence of additional embodiments that also incorporate the recitedfeatures. The drawing figures are not necessarily to scale. Certainfeatures and components disclosed herein may be shown exaggerated inscale or in somewhat schematic form, and some details of conventionalelements may not be shown in the interest of clarity and conciseness.

The terms “including” and “comprising” are used herein, including in theclaims, in an open-ended fashion, and thus should be interpreted to mean“including, but not limited to . . . .” Also, the term “couple” or“couples” is intended to mean either an indirect or direct connection.Thus, if a first component couples or is coupled to a second component,the connection between the components may be through a direct engagementof the two components, or through an indirect connection that isaccomplished via other intermediate components, devices and/orconnections. If the connection transfers electrical power or signals,the coupling may be through wires or other modes of transmission. Insome of the figures, one or more components or aspects of a componentmay be not displayed or may not have reference numerals identifying thefeatures or components that are identified elsewhere in order to improveclarity and conciseness of the figure.

Electric submersible pumps (ESPs) may be deployed for any of a varietyof pumping purposes. For example, where a substance does not readilyflow responsive to existing natural forces, an ESP may be implemented toartificially lift the substance. Commercially available ESPs (such asthe REDA™ ESPs marketed by Schlumberger Limited, Houston, Tex.) may finduse in applications that require, for example, pump rates in excess of4,000 barrels per day and lift of 12,000 feet or more.

To improve ESP operations, an ESP may include one or more sensors (e.g.,gauges) that measure any of a variety of physical properties (e.g.,temperature, pressure, vibration, etc.). A commercially available sensoris the Phoenix MultiSensor™ marketed by Schlumberger Limited (Houston,Tex.), which monitors intake and discharge pressures; intake, motor anddischarge temperatures; and vibration and current leakage. An ESPmonitoring system may include a supervisory control and data acquisitionsystem (SCADA). Commercially available surveillance systems include theespWatcher™ and the LiftWatcher™ surveillance systems marketed bySchlumberger Limited (Houston, Tex.), which provides for communicationof data, for example, between a production team and well/field data(e.g., with or without SCADA installations). Such a system may issueinstructions to, for example, start, stop, or control ESP speed via anESP controller.

The conventional method for detecting pumping system failures andgenerating warnings or alarms regarding pumping system failures may notaddress issues early enough and are subject to false alarms. Further,much of the evaluation (e.g., of alarms or warnings) is performed bysurveillance engineers and thus is prone to error, delay, andinconsistency. As a result, certain errors may be miscategorized oroverlooked, leading to ESP events going unnoticed. In the case wheredelay exists in identifying and/or categorizing an event, it may be toolate to take any corrective action to remedy the ESP issue.

To overcome these deficiencies of conventional ESP event detection, andin accordance with various embodiments of the present disclosure,systems and methods are described in that facilitate improvement ofoperations with respect to an electric submersible pumping systemdeployed in a well environment or other environment. The presentdisclosure enables monitoring of a plurality of parameters to obtaindata regarding operation of the electric submersible pumping system. Thedata is processed according to an automatic event detection algorithm.Based on the processing of data a specific event (or events) may bedetected, which may indicate potential problems with operation of theelectric submersible pumping system. The present disclosure furtherenables automatic adjustment of the electric submersible pumping systemupon detection of the event or events, which may be carried out in realtime.

As described in greater detail below, the Automatic Event Detection(AED) algorithm may be used on a processor-based system, such as acomputer system, to process data related to operation of an electricsubmersible pump (ESP). By way of example, the data may be obtained fromsurface and downhole measurements. In an embodiment, an ESP is installeddownhole in a well where oil is available, and the ESP is used to liftoil from there to the surface. An ESP failure leads to cost ofreplacement and deferred production. As used in the description herein,an ESP event refers to a situation in which the ESP potentially can bedamaged.

Examples of events which potentially can damage the ESP include eventsinducing stress to the ESP such as operating against a closed valve,operating below minimum frequency to lift the fluid to surface, gaslocking, and/or other potentially damage-inducing events. Variousmeasurements may be used for the detection of the event, and examples ofthose measurements include measurements of Drive Frequency, MotorCurrent, Discharge Pressure, Intake Pressure, and Motor Temperature,which are explained in greater detail below. If additional measurements,such as measurements including derived parameters (e.g., from algorithmsor modelling) or completion information (e.g., pump curves, inflowperformance) are available, those measurements may be used to furtherenhance the event detection. However, the described event detectionsystems and methods are robust enough to function even in situations inwhich such additional measurements are not available.

The described systems and methods for event detection are useful in avariety of well applications. For example, in the case of an offshoreESP failure, the cost of a workover may be in excess of $1 million.Additionally, the deferred production that results from an ESP failurecan cost $200,000-$500,000 per day. Thus, it is advantageous to detectevents early and automatically, with reduced false alarms, and withsubsequent intervention in the ESP operation to prevent failure of theESP. In some applications, the system may utilize a closed loop controlso the system can react in pseudo-real-time to prevent failure during anearly mode of detection. The detection of a specific event or events isinput into the closed loop control. Thus, when an event (or events) isdetected and categorized according to the AED, remedial actions may beautomatically carried out by the system to prevent the failure. Use ofthe AED further helps reduce the cost of monitoring wells whileproviding better protection for the ESP.

In some applications, physics knowledge and contextual information alsocan provide helpful data which is useful in detecting and categorizing aparticular event or events. The physics and contextual information mayinclude a variety of information related to the oilfield and/or the ESP.For example, each ESP is designed differently and, therefore, hasdifferent physics properties. Information collected during fieldexploration and drilling processes also is helpful in establishingdifferent baselines for different oilfields and different wells. Byintegrating this information into a mathematical model, the processingof data is facilitated with respect to event detection and protection ofthe ESP.

Embodiments described herein are distinct relative to existingalarm-based systems. For example, the AED may be used to detect an eventrather than a failure. The difference between an event and a failure isthat often multiple events lead to a failure. Embodiments describedherein also may utilize the AED to target a variety of differentproblems relative to existing systems. Additionally, embodimentsdescribed herein may combine the available channels to produce animproved detection result.

Generally, events are the conditions in which the pump is operatingunder stress, which most of the time happens when the pump is operatingat low/no flow condition. Such events are referred to as low flow due toinsufficient lift (or “LF-IL”) events and low flow due to gasinterference (or “LF-GI”) events. In the occurrence of an LF-IL event,the pressure the pump generates is not enough to lift fluids to thesurface, either due to the pump running below sufficient frequency ordue to excessive back pressure. In LF-GI events where excessive gas ispresent in the system, the pump might struggle to cycle through gas,which can potentially even lead to locking the pump, which may be aseparate event referred to as low flow due to gas locking (or “LF-GL”).Under these conditions, the reduced fluid velocity flowing past themotor may not be sufficient to cool the motor and the pump.Additionally, as the flow rate tends toward zero, so too does systemefficiency, and thus any energy consumed by the ESP 110 is converted tolocalized heat. Both phenomena lead to overheating, resulting in pumpfailure unless a timely intervention is performed.

Referring now to FIG. 1, an example of an ESP system 100 is shown. TheESP system 100 includes a network 101, a well 103 disposed in a geologicenvironment, a power supply 105, an ESP 110, a controller 130, a motorcontroller 150, and a variable speed drive (VSD) unit 170. The powersupply 105 may receive power from a power grid, an onsite generator(e.g., a natural gas driven turbine), or other source. The power supply105 may supply a voltage, for example, of about 4.16 kV.

The well 103 includes a wellhead that can include a choke (e.g., a chokevalve). For example, the well 103 can include a choke valve to controlvarious operations such as to reduce pressure of a fluid from highpressure in a closed wellbore to atmospheric pressure. Adjustable chokevalves can include valves constructed to resist wear due to highvelocity, solids-laden fluid flowing by restricting or sealing elements.A wellhead may include one or more sensors such as a temperature sensor,a pressure sensor, a solids sensor, and the like.

The ESP 110 includes cables 111, a pump 112, gas handling features 113,a pump intake 114, a motor 115 and one or more sensors 116 (e.g.,temperature, pressure, current leakage, vibration, etc.). The well 103may include one or more well sensors 120, for example, such as thecommercially available OpticLine™ sensors or WellWatcher BriteBlue™sensors marketed by Schlumberger Limited (Houston, Tex.). Such sensorsare fiber-optic based and can provide for real time sensing of downholeconditions. Measurements of downhole conditions along the length of thewell can provide for feedback, for example, to understand the operatingmode or health of an ESP. Well sensors may extend thousands of feet intoa well (e.g., 4,000 feet or more) and beyond a position of an ESP.

The controller 130 can include one or more interfaces, for example, forreceipt, transmission or receipt and transmission of information withthe motor controller 150, a VSD unit 170, the power supply 105 (e.g., agas fueled turbine generator or a power company), the network 101,equipment in the well 103, equipment in another well, and the like. Thecontroller 130 may also include features of an ESP motor controller andoptionally supplant the ESP motor controller 150.

The motor controller 150 may be a commercially available motorcontroller such as the UniConn™ motor controller marketed bySchlumberger Limited (Houston, Tex.). The UniConn™ motor controller canconnect to a SCADA system, the espWatcher™ surveillance system, etc. TheUniConn™ motor controller can perform some control and data acquisitiontasks for ESPs, surface pumps, or other monitored wells. The UniConn™motor controller can interface with the Phoenix™ monitoring system, forexample, to access pressure, temperature, and vibration data and variousprotection parameters as well as to provide direct current power todownhole sensors. The UniConn™ motor controller can interface with fixedspeed drive (FSD) controllers or a VSD unit, for example, such as theVSD unit 170.

In accordance with various examples of the present disclosure, thecontroller 130 may include or be coupled to a processing device 190.Thus, the processing device 190 is able to receive data from ESP sensors116 and/or well sensors 120. Although shown schematically at certainlocations, it should be appreciated that the ESP sensors 116 and/or wellsensors 120 may be situated in various locations among the system 100.These sensors 116, 120 may be used to measure various parametersdisclosed above, such as drive current, motor temperature, pump intakepressure, pump discharge pressure, static intake pressure, drivefrequency, pump flow rate, and the like.

As will be explained in further detail below, the processing device 190analyzes the data received from the sensors 116 and/or 120, possiblywith the addition of sensors from the VSD 170 and the controller 130, toprovide enhanced and automated event detection, which may then be usedto control the operation of the ESP 110 to prolong its life and/or avoiddowntime of the ESP 110. The detection of an ESP 110 event may bepresented to a user such as a surveillance center employee or a wellsite operator through a display device (not shown) coupled to theprocessing device 190, through a user device (not shown) coupled to thenetwork 101, or other similar manners. Generally, the processing device190 may also be referred to as executing an AED engine to carry outvarious functionality of that engine described herein. The scope of thepresent disclosure is not intended to be limited to any particularlocation of various system 100 components; for example, processing andevent detection may be carried out at the well site, in a cloudenvironment; at a remote surveillance center, and in any number ofvarious centralized and distributed arrangements.

In some embodiments, the network 101 comprises a cellular network andthe user device is a mobile phone, a smartphone, or the like. In theseembodiments, the detection of an event of the ESP 110 may be transmittedto one or more users physically remote from the ESP system 100 over thecellular network 101. In some embodiments, the detection of an event ofthe ESP 110 may indicate that the ESP 110 is expected to remain in itsnormal operating mode, or may be a warning of varying severity that afault, failure, or degradation in ESP 110 performance is expected.

Regardless of the type of ESP 110 event detected, certain embodiments ofthe present disclosure may include taking a remedial or other correctiveaction in response to detection of an event that may lead to a decreasein ESP 110 performance or to an outright failure of the ESP 110. Theaction taken may be automated in some instances, such that detection ofa particular type of event automatically results in the action beingcarried out. Actions taken may include altering ESP 110 operatingparameters (e.g., operating frequency) or surface process parameters(e.g., choke or control valves) to prolong ESP 110 operational life,stopping the ESP 110 temporarily, and providing a warning to a localoperator, control room, or a regional surveillance center.

Referring generally to FIG. 2, an example is provided which illustratesthe LF-IL condition 200 when the pump is operating at insufficientfrequency. In other words, the pump does not provide enough pressure tolift the fluids to the surface. Consequently, the flow rate through thepump is reduced and may provide insufficient cooling for the pump andmotor. Similarly, in LF-GI conditions, gas may block flow through thepump. This situation results in the same consequences as the LF-ILcondition.

As described herein, systems and methods for event detection may beapplied at selected ESP well applications where certain parametermeasurements (e.g., by way of sensors 116, 120) are available. Examplesof those measurements include drive frequency, motor current, dischargepressure, intake pressure, and motor temperature, as discussed above.Applications may include offshore wells in which the parametermeasurements are available and protection of the ESP is very important,for example due to cost considerations as explained above.

In certain cases, each of the plurality of measurement channels may havethe same sampling frequency and the same starting time. However, oftentimes in reality, each channel may have its own sampling frequency itsown starting time. Further, the sampling rate may be changed duringoperation, for example due to operator intervention. As a result,certain data may be missing and examples of the present disclosurepre-process the data to achieve a complete dataset. First, for example,a same sampling rate is applied to the whole dataset (i.e., the datafrom a number of different measurement channels). If the actual samplingrate is under-sampling for a specific channel, then a linear regressionmodel may be applied to impute the missing values. On the other hand, ifthe actual sampling rate is over-sampling, then a moving average windowmay be applied to down-sample the channel. That is, in the event dataneeds to be re-sampled, regardless of whether it is to up- ordown-sample, such re-sampling may be carried out.

During a LF-IL event and without the appearance of gas, a hydraulicsignature may be very strong or pronounced because the properties of thefluid are consistent throughout the tubing/casing. For this reason, todetect LF-IL, examples of the present disclosure may focus on orprioritize hydraulic parameters such as P_(d) and P_(i) and ΔP incertain cases. In LF-GI events, on the other hand, fluid properties maychange constantly due to the appearance of gas in the mixture, whichresults in weak hydraulic signatures and a lack of correlation between aparticular hydraulic signature and the occurrence of a LF-GI event.However, other electrical channels such as drive current, or Amps, maybe a strong indicator of a LF-GI event because it reflects the load ofthe pump which is directly related to the type of fluid pumped. For thisreason, to detect LF-GI, several parameters may be observed, includingP_(d), P_(i), Amps, f, T_(m), and t.

It should be appreciated that various ones of the observable parametersdiscussed herein may be monitored and leveraged to detect an ESP 110event. For example, the inside motor temperature may be an importantindicator for detecting whether the pump is running below a certaintarget efficiency. Similarly, P_(d) may be an indicator for the heightof the fluid column above the pump. Discharge pressure also may reflectany restriction above the pump, slugging effect, changing of water cut,or other restrictions. As another example, time may have an effect oncertain other variables, particularly P_(i). That is, the reservoir isdepleted gradually over time, and thus has an effect on P_(i) sinceP_(i) is related to reservoir conditions as well as operation of thepump. ΔP may be an important indicator of flow rate through the pump.WHT may be used as an important indicator to determine if there is flowon the surface. Choke position may be used to provide informationhelpful in understanding operating parameter changes, as the chokeposition controls the flow rate of the well. The importance of other ofthe observable parameters, as well as parameters not presentlydiscussed, but subject to observation in proximity to a well site, maybe utilized for event detection and should be considered within thescope of the present disclosure.

Examples of the present disclosure rely on the fact that when an eventoccurs, signals of one or more measured parameters tend to deviate fromtheir “normal values.” FIG. 3 illustrates signal waveforms 300 for anLF-IL event. At time 302, the pump is turned off and an intake pressurevalue 306 returns approximately to a static intake pressure value 309.It should be appreciated that the static intake pressure value 309 isoften highly correlated to a reservoir pressure, and the amount ofcorrelation will vary with distance between the reservoir and the pumpintake. Similarly, a discharge pressure value 307 also returns toroughly the intake pressure value 306 when the pump is off at 302, whichalso happens to be approximately the static intake pressure value 309. Areference signal 308 indicates the determined reference value for intakepressure 306 when the pump is running. In certain examples, thereference signal value 308 may be determined experimentally (e.g.,during an early phase of ESP 110 operation when conditions are known andthe ESP 110 is operationally sound), while in other examples thereference signal value 308 may be determined or predicted based onphysics laws taking into account various factors such as static intakepressure, ESP 110 parameters, and the like. In accordance with examplesof the present disclosure, when the actual measurement of intakepressure 306 deviates from the reference signal value 308, an event maybe detected.

Generally speaking, as time goes by, the reservoir is depleting and thusit may be expected to see the static intake pressure 309 slowly decline.Similarly, this may result in the intake pressure 306 graduallydeclining as well, and thus the intake pressure reference value 308 maybe adjusted over time to reflect this expected gradual decline.

As noted above, at time 302 a large shift in various signals occurs,which corresponds to the pump turning off. This can be confirmed by themotor speed value 310 dropping to zero. As explained, the intake anddischarge pressures 306, 307 converge at a value that approximates thestatic pressure of the well at the pump depth since no pumping isoccurring. In some embodiments, the AED engine may learn during thistime period that if, during operation (i.e., motor speed is non-zero),observed intake and discharge pressures 306, 307 are close to the staticintake pressure 309, a low- or no-flow event may be occurring, and analarm may be generated to alert an operator of such event.

Subsequently, at time 304, the pump is restarted as indicated by themotor speed 310 resuming a non-zero value. Typically, the motor speed310 or frequency is ramped up to the desired operating frequency. Notethat while initially the intake pressure 306 drops, the intake pressure306 then begins to ramp back up to nearly the static intake pressure 309(and deviates from its expected reference value 308). This suggests thatthe pump is not being operated at a high enough frequency. Thus, themotor speed 310 is increased, which results in the intake pressure 306recovering to its reference value 308. To summarize, the actual signalfor intake pressure 306 is compared to its reference value 308 and wherea deviation occurs unexpectedly (i.e., not due to other contextualinformation such as the pump being turned off), an event is identified.In this particular example, the root cause for the event was the motorfrequency 310 being too low and subsequent remedial action of increasingthe motor frequency 310 rectified the situation.

As another example, had the discharge pressure 307 instead deviatedabove its reference signal 311, the AED engine may determine that thelow-flow event corresponds to a restriction above the pump. Similarly,other contextual information such as a valve above the pump being shutor partially shut may be leveraged by the AED engine to determinewhether an alarm should be raised or whether the event is expected inthat certain context. Yet another example, which is not depicted forsimplicity, is one in which pressures are behaving erratically and motoramperage drops, an event corresponding to the presence of gas may beidentified and alerted.

Generally speaking, the flow rate Q from the reservoir is proportionalto the difference between static intake pressure and intake pressure(i.e., P_(i (static))−P_(i)) in steady state conditions. Thus, when theESP 110 is running and that difference is close to 0, the pump flow ratewill trend to be close to 0 as well. This provides a strong indicator ofa LF-IL event. In certain cases, a physics model is applied to predictthe “normal values” for various parameters, which are the values thesignals should be at for a particular given condition. These values arereferred to as reference values or reference signals. In examples of thepresent disclosure, correlations between reference signals are modeledand fluid type may be purposely ignored. As a result, since the fluid isnot considered in the correlations, whenever a change in the fluidoccurs (e.g., gas formation, lower flow rate, or certain other changes),the real measurements change while the reference values stay the same.

The below discussion is directed toward reference signal modeling, whichmay be specific for the selected channels, but the same principle may beapplied to model other channels as well. As such, the reference modelingalgorithm is general and can be applied in different areas. Sincepressures generally depend strongly on motor frequency and time, a leastsquare regression may be used to predict values.

Over time, well performance gradually changes, which affects channelssuch as static intake pressure, intake pressure, average drive current,and the like. To adapt those signals to changes over time, certainexamples construct a time dependency reference signal module given bythe following:

RefT=func(1,t)

In this module, the reference signal is described as a function of timeonly, which captures the correlation between raw measurements with timeand reflects the relationships in its coefficients. These examples fit alinear curve through the raw measurements and calculate coefficients ofthe curve by minimizing the residuals between the curve and the real,raw measurements. For example, let X be the observations, Coeff be theset of coefficients a, b, c, d, e and R be the residual:

R=X−(a+bt _(1:t-1))

As explained above, the residuals are minimized using a least squareregression, outputting the coefficients Coeff

${\underset{Coeff}{argmin}\mspace{14mu} {R({Coeff})}}:=\left\{ {{Coeff}{\forall{{{Coeff}^{\prime}\text{:}{R\left( {Coeff}^{\prime} \right)}} \geq {R({Coeff})}}}} \right\}$

Subsequently, reference values are reconstructed based on t and on Coeff

Ref_(t)=(a+bt _(t))

For channels that depend not only on time, but also on drive frequency,another module may be employed to model such dependencies. Such channelsinclude, but are not limited to, discharge pressure, intake pressure,well head pressure, and average drive current. Correlation may bediscovered based on real measurements of those channels. Therefore, insituations where there is no correlation between the channels, this toocan be automatically learned and reflected by the determinedcoefficients. Specifically, the coefficient associated with the drivefrequency will be negligible. To adapt such signals to changes over timeand frequency, certain examples construct a time and frequencydependency reference signal module given by the following:

Ref_(tf)=(a+bt _(t) +cf _(t) +df _(t) ² +ef _(t) ³)

The set of Coeff{a, b, c, d, e} may be derived using the samemethodology explained above with respect to time-dependent channels.

It should be appreciated that the same principle applies for T_(m) andAmps. T_(m) is a function of Amps because part of the power provided bythe ESP 110 is converted into heat, whereas the Amps itself depends onthe frequency f.

As one example of the above determination of reference signals, considerreference signals for P_(d) and P_(i). When the event detection (i.e.,AED engine) first begins, data relating to real values of variousparameters are collected, which allows the above coefficients to bedetermined. Then, for subsequent restarts, the AED engine first usesreference values from the time-based reference. For example, when theESP 110 is restarted, the frequency may be gradually increased whilestill remaining below a minimum frequency. As a result, LF events arenot detected if the reference value is calculated based on frequency,which is why the time-based reference value is utilized to ensure LFevents at restart can be detected.

During normal operating conditions, the AED engine uses reference valuesfrom the time- and frequency-based model, explained above (i.e.,Ref_(tf)). This helps to avoid a LF false alarm when, for example, rigoperators change a frequency to operate at another stable flow rateduring normal operations. In this example, outliers are not fed to theinput of the AED learning engine (e.g., by filtering), and the AEDlearning engine uses a selected and potentially programmable number ofobservations (e.g., 10,000 observations) to calculate coefficients.Generally, a greater number of observations improve the confidence inthe reference values and the AED engine prediction results.

As discussed above, when certain events occur, signals tend to deviatefrom their reference values. These deviations, when not explainable byother context information (e.g., shutting off the pump, opening orclosing various valves, and the like), are used to detect events.Additionally, examples of the present disclosure estimate the staticintake pressure and utilize in LF-IL event detection. Further, areference value for f is calculated to detect a LF event when the pumpis running below its minimum frequency. The following are examples ofevents that may be selected for detection by the AED engine.

For example, the AED engine may detect an event when, at restart, theintake pressure increases back to the static intake pressure afterdecreasing and ΔP decreases. The AED engine may also detect an eventwhen, during normal operation, the intake pressure increases back to thestatic intake pressure and ΔP decreases.

The AED engine may detect an event when, during normal operation,pressures are fluctuating. For example, intake pressure tends toincrease back to static intake pressure and ΔP tends to decrease. Thisdetects a gas slugging situation where a large volume of gas causes thefluctuation of pressures and decreases the flow rate. The AED engine maydetect an event when, during normal operation, ΔP is too low compared toits reference value. The AED engine may detect an event when, atstart-up (i.e., when the pump is first installed and turned on), theintake pressure increases up to the static intake pressure afterdecreasing, which suggests the annulus emptying. The AED engine may alsodetect an event when, during normal operation, f is significantly lowerthan its reference value. One skilled in the art, particularly withregard to ESP 110 surveillance, would readily appreciate that a similarconcept may be applied to detecting other low-flow events, such as aclosed valve, gas interference, or the like.

Another example of the AED's ability to detect an event is where theevent corresponds to a scenario in which the AED engine shouldreestablish reference values. For example, when the pump is off, if thestatic pressure is fluctuating significantly, it likely indicates somewell intervention that could modify its productivity. In this scenario,the AED engine may restart and reestablish reference signals for variousparameters without using previously established reference signals.

Referring generally to FIG. 4, one example of an AED algorithm 400 isillustrated. The AED algorithm 400 may be operated on a suitableprocessor-based system, such as a computer-based system located at thesurface or another suitable location as explained with respect toFIG. 1. FIG. 4 provides a flowchart illustrating an example of the AEDalgorithm 400, although the algorithm 400 may be adjusted to accommodatechanges and different types of applications.

When the AED algorithm 400 first begins running on a particular ESP 110,there is no or an insignificant signal history from which to establishreference signals. Thus, the algorithm 400 initially cannot generatemeaningful reference signals until sufficient observation(s) have beengathered from one or more channels of observable parameters beingmonitored, as shown in blocks 402, 404. In certain embodiments, thereadiness of the algorithm 400 to begin comparing observed parameters toreference signals to detect an event may be obtained by examining therank of an input matrix.

For example, in the case of a reference signal that depends on both timeand frequency as described above, input signals may be arranged into a5×N matrix in block 406, as follows:

$\quad\begin{bmatrix}1 & t & F_{0}^{1} & F_{0}^{2} & F_{0}^{3} \\1 & t & F_{1}^{1} & F_{1}^{2} & F_{1}^{3} \\{\vdots \;} & \vdots & \vdots & \vdots & \vdots \\1 & t & F_{N}^{1} & F_{N}^{2} & F_{N}^{3}\end{bmatrix}$

Block 408 determines whether full rank of the matrix is obtained. Fullrank is obtained when the updated matrix formed in block 406 containsfive or more unique rows that cannot be expressed as a linearcombination of other rows of the matrix. Typically, where the algorithmhas been acquiring data in blocks 402 and 404 for a reasonable amount oftime, and potentially at different operating frequencies, the matrixformed in block 406 will be full rank, and the algorithm 400 continuesin block 410 with identifying an AED engine as ready to compare observedresults to learned reference signals in block 412.

However, in order to facilitate detection during this time, a windowedrunning average may be employed, which is gated based on how long thedetection has been running. That is, when a full rank matrix is notavailable in block 408, the algorithm 400 continues in block 414 todetermine whether enough time has passed. If a certain amount of timehas not passed, the algorithm 400 proceeds to block 420 to confirm thatthe AED engine is not yet ready to compare observed results to referencesignals, and additional observed parameters are acquired in blocks 402,404 as explained above. However, if in block 414 sufficient time haspassed (i.e., a meaningful average has been established for various onesof the channels of observed parameters), those averages are used asreference signals in block 412 until the matrix reaches full rank (i.e.,in decision block 408), at which point the reference values are computedas described above in block 412.

Similar to LF-IL detection, in LF-GI detection, the AED enginedetermines reference signals for the plurality of channels. In certainexamples of the present disclosure, the AED engine referencedetermination may differ between the two algorithms in active states,the training phases, and the input signals. While LF-IL event detectionfocuses on detection of low flow during startup, the LF-GI eventdetection detects the low flow during steady state operation of thepump. The reason for this distinction is that LF-IL typically occursduring startup when the pump is restarted and therefore the pump may beoperating at a lower frequency for a long time. By contrast, for LF-GIevents, gas commonly exits from the liquid when the pump is off. Thus,when the pump is restarted, the liquid in the tubing normally does notcontain gas. For this reason, LF-GI happens during steady stateproduction.

In some applications, the AED algorithm comprises a training phase.Specifically, a logistic regression classification model may be used totrain the data with “good” and “bad” samples for LF-GI detection. Inlogistic regression, a sigmoid function is utilized to calculate theprobability of a given sample belonging to the “good” or the “bad”class:

${h_{\theta}(x)} = \frac{1}{1 + e^{{{- \theta^{T}}x}\;}}$

Where x refers to the residuals from the channels. The sigmoid functionpredicts the label for each sample. Based on this prediction, a costfunction may be calculated and minimized:

${J(\theta)} = \left\lbrack {{{- \frac{1}{m}}{\sum\limits_{i = 1}^{m}\; {y^{(i)}{\log\left( {{h_{\theta}\left( x^{(i)} \right)} + {\left( {1 - y^{(i)}} \right)\log \; 1} - {h_{\theta}\left( x^{(i)} \right)}} \right\rbrack}}}} + {\frac{\lambda}{2\; m}{\sum\limits_{j = 1}^{n}\theta_{j}^{2}}}} \right.$

FIG. 5 illustrates the result, which includes a decision boundary 502that minimizes the cost function. In this example, the boundary 502serves as the detection threshold for the LF-GI event. Measurements 504correspond to normal operating conditions and are on a first side of theboundary 502. By contrast, measurements 506 correspond to gasinterference (i.e., a LF-GI event) and are on the other side of theboundary 502 from the normal measurements 504. Any measurements thatresult in a data point in the “bad” region, or where observations causethe boundary 502 to be exceeded, may be treated with concern and analarm raised if necessary. In addition to the boundary 502 above, LF-GIalso may be detected with a rise of T_(m). The T_(m) also may be usedseparately as an indicator of the ESP 110 health. Whenever there is asignificant raise of T_(m) from its referenced value, an alarm may beraised.

Depending on the application, embodiments described herein may be usedwith many types of well equipment and, in some applications, non-wellequipment. The AED algorithm/engine may be adjusted to accommodateprocessing of many types of other and/or additional data. Additionally,the data may be adjusted or otherwise processed according to variousadditional models and/or processing techniques to provide desired datato the AED algorithm. In many applications, automated control of the ESPis initiated based on the processing of data by the AED algorithm. Thespecific and automated changes to operation of the ESP may be determinedaccording to a variety of individual or combined events detected bysuitable sensors located downhole and/or at the surface.

Turning now to FIG. 6, a method 600 of monitoring operation of the ESP110 is described in accordance with various examples of the presentdisclosure. The method 600 utilizes the above-described referencesignals and comparison of measured values of various parameters to thosereference signals to provide improved detection of various events withreduced reliance on operator decision-making and reduced false alarms.

The method 600 begins in block 602 with receiving data signalsindicating values for a plurality of parameters regarding operation ofthe ESP system. The method 600 continues in block 604 with establishinga reference signal associated with at least some of the plurality ofparameters. As noted above, certain parameters such as intake anddischarge pressure, motor current, and motor speed may becommonly-employed indicators of whether an event is occurring. Theestablished reference signals serve as a value, learned over time, thatindicates a level of normal operation for a given parameter.

The method 600 then continues in block 606 with detecting a deviation ofone of the parameters from its reference signal. Of course, in somecases more than one parameter may deviate from its reference signal aswell, and such scenarios are within the scope of the AED engine andmethod 600 of the present disclosure.

As explained, certain observed parameters may gradually change over time(e.g., intake pressure gradually declines as the reservoir is depleted),and these changes may occur over periods of weeks, months, or even more.Such changes do not necessarily correspond to an event. However, forpurposes of comparison to the reference signal, the reference signalshould be updated to reflect such gradual changes. Thus, as a result ofthe deviation detected in block 606 having a rate of change below athreshold, the method 600 continues in block 608 with updating the valueof the reference signal to reflect the deviation.

Of course, changes to observed parameters may also occur in a shorterperiod of time (e.g., intake pressure rising sharply to the staticintake pressure due to insufficient lift), such a as a period of hoursor days. When the rate of change of deviation from the reference signalis faster than would be expected under normal operating conditions, thechanges likely correspond to an event, such as a LF-IL event in theexample above. Thus, as a result of the deviation detected in block 606having a rate of change above a threshold, the method 600 continues inblock 610 detecting an event and generating an indication of the event.

Different events may bear more or less correlation to certain observedparameters, and thus in some cases the method 600 uses a certain set ofparameters to identify a first event such as LF-IL, but utilizes adifferent set of parameters to identify a second event such as LF-GI.Certain overlap in the sets of parameters may exist; however, algorithmsand particular parameters themselves may differ from identifying onetype of event to another type of event.

The method 600 may also include pre-processing one or more of the datasignals to detect a data quality deficiency. The cause of the qualitydeficiency may be identified and corrected; however, in cases where thedeficiency cannot be corrected, a user may be alerted to the existenceof the quality deficiency. One advantage of the disclosed AED engine andmethod 600 is the ability to operate in real time. Thus, data changeevents are monitored that could be sporadic for one channel and regularfor others. Data change events and/or data quality deficiency may resultfrom a number of different factors. Indeed, the data acquisition occursover a chain starting from the downhole gauge, which is then connectedto a surface data acquisition system through a 3-phase power cable. Datacollected there are transmitted from the well site to various operatorsand surveillance centers. If any of those components malfunctions, dataquality will be affected.

Various data quality problems may be detected by the AED engine andmethod 600. For example, the method 600 may identify missing physicaldata, where data is not received within some expected time period (e.g.,10 minutes), which can of course be configured depending on application.Missing physical data may occur as a result of a lost communication link(e.g., a wired or wireless link over which data is transmitted), a gaugedisconnected from a power supply, and the like. The method 600 may alsoidentify missing contextual data, such as choke/valve positions, wellintervention, and the like. The method 600 may also identify sensorfailures (e.g., due to lack of power, other electrical faults, brokenpressure subassemblies, or other general malfunctions of sensors).

The method 600 may also detect a frozen data condition, where amonitored channel displays zero variation in reported values for atleast a predetermined time period. Certain outlier boundaries may alsobe respected by the method 600, where a sensor reporting values outsideof what is physically possible for a particular context may be flaggedand identified as a quality deficiency. Additionally, certain eventssuch as operator intervention may cause a scale change in theinterpretation of a sensor value (e.g., reconfiguring a pressure valueas bar rather than psi), which results in a large deviation in what theAED believes it is observing, despite little to no change in thephysical value being measured. Another scale change may occur due tohardware, such as a number of taps on a transformer at the well site.

Various other electrical faults may also be detected, which affect thereliability of the downhole measurements generated by the gauge as it ispowered by DC current transmitted from the surface. These faults may bedetected by gauge reference current values (zero-scale, full-scale, andactive leakage) being compared against their nominal common-mode and, ifavailable, differential ranges for a given gauge type. Any significantdeviation from the established operating parameters for a given samplemay trigger an electrical fault alarm that indicates a data qualitydeficiency may be present from that gauge.

The method 600 may also identify a root cause of the detected event andprovide a suggested operation to a user. For example, the root cause maybe the actual event that leads to a low/no flow condition, such asinsufficient lift, flow restriction, the pump being off, or gasinterference. These are exemplary root causes, and of course othercauses of a low/no flow condition are similarly within the scope of thepresent disclosure. The operation suggested to the user may include anumber of remedial actions, for example, such as increase the drivefrequency, adjust a choke, check various valve positions, lower thedrive frequency, apply a closed-loop control to the frequency, orvarious other actions and/or combinations of actions. The suggestedoperation may be a modification of one or more operating parameters ofthe ESP 110 or system 100 more generally, as explained above. In somecases, the method 600 also includes refining a determination of the rootcause based on observed parameters (e.g., changes to observedparameters) after the operating parameters are modified by an operator.For example, if a low flow event is detected and at first appears to bedue to an obstruction caused by the choke position, the suggestedoperation may be to open the choke by a particular amount. However, ifopening the choke does not solve the low flow condition, the root causemay be determined to be another obstruction above the ESP but below thechoke.

In addition to providing an alarm and diagnosis or explanation of theevent, the method 600 may also cause various other adjustments to theoperation of the ESP. For example, a recommendation may be provided tothe operator to manually adjust various aspects of the system. Asanother example, automatic adjustment of steady state parameters (i.e.,semi-closed loop) may be carried out. Yet another example includesenabling/disabling different closed loop controllers depending on thedetected event type or normal operation.

The method 600 may also include prompting a user for input following adeviation of one of the parameters from its reference signal. Forexample, even if the method 600 or AED engine detects a deviation likelyto suggest an event has occurred, a user in the field may haveadditional awareness not available to the AED engine, and may confirmthat the change in an observed parameter was expected, which wouldresult in the reference value for that parameter being updated ratherthan an alarm being triggered. In this way, the learning ability of theAED engine and method 600 is enhanced on an application-by-applicationbasis, rendering the AED engine and method 600 suitable to an evenbroader range of applications as the function is tailored over the lifeof the AED engine based on its own ability to learn and correctreference signals as well as the ability for user input to be leveragedto tailor reference signals. This enhanced flexibility and learningaspect results in a further reduction in false alarms even among widelyvariable application conditions.

Some of the methods and processes described above, including processes,as listed above, can be performed by a processor (e.g., processor 190).The term “processor” should not be construed to limit the embodimentsdisclosed herein to any particular device type or system. The processormay include a computer system. The computer system may also include acomputer processor (e.g., a microprocessor, microcontroller, digitalsignal processor, or general purpose computer) for executing any of themethods and processes described above.

The computer system may further include a memory such as a semiconductormemory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-ProgrammableRAM), a magnetic memory device (e.g., a diskette or fixed disk), anoptical memory device (e.g., a CD-ROM), a PC card (e.g., PCMCIA card),or other memory device.

Some of the methods and processes described above, as listed above, canbe implemented as computer program logic for use with the computerprocessor. The computer program logic may be embodied in various forms,including a source code form or a computer executable form. Source codemay include a series of computer program instructions in a variety ofprogramming languages (e.g., an object code, an assembly language, or ahigh-level language such as C, C++, or JAVA). Such computer instructionscan be stored in a non-transitory computer readable medium (e.g.,memory) and executed by the computer processor. The computerinstructions may be distributed in any form as a removable storagemedium with accompanying printed or electronic documentation (e.g.,shrink wrapped software), preloaded with a computer system (e.g., onsystem ROM or fixed disk), or distributed from a server or electronicbulletin board over a communication system (e.g., the Internet or WorldWide Web).

Alternatively or additionally, the processor may include discreteelectronic components coupled to a printed circuit board, integratedcircuitry (e.g., Application Specific Integrated Circuits (ASIC)),and/or programmable logic devices (e.g., a Field Programmable GateArrays (FPGA)). Any of the methods and processes described above can beimplemented using such logic devices.

Although only a few example embodiments have been described in detailabove, those skilled in the art will readily appreciate that manymodifications are possible in the example embodiments without materiallydeparting from the scope of the present disclosure. Features shown inindividual embodiments referred to above may be used together incombinations other than those which have been shown and describedspecifically. Accordingly, all such modifications are intended to beincluded within the scope of this disclosure as defined in the followingclaims.

The embodiments described herein are examples only and are not limiting.Many variations and modifications of the systems, apparatus, andprocesses described herein are possible and are within the scope of thedisclosure. Accordingly, the scope of protection is not limited to theembodiments described herein, but is only limited by the claims thatfollow, the scope of which shall include all equivalents of the subjectmatter of the claims.

What is claimed is:
 1. A method for monitoring operation of an electricsubmersible pump, comprising: receiving, by a processor from one or moresensors, data signals indicating values for a plurality of parametersregarding operation of the electric submersible pumping system;establishing, by the processor and for at least some of the plurality ofparameters, an associated reference signal; detecting, by the processor,a deviation of one of the parameters from the reference signalassociated with that parameter; as a result of the deviation having arate of change below a predetermined threshold, updating the value ofthe reference signal to reflect the deviation; and as a result of thedeviation having a rate of change above the predetermined threshold,detecting an event and generating an indication of the event; whereinthe indication of the event further depends on a type of the parameterand a direction of the deviation from the reference signal.
 2. Themethod of claim 1 wherein the event comprises one type of a plurality ofevent types and wherein a set of parameters observed to detect an eventof a first type differs from a set of parameters observed to detect anevent of a second type.
 3. The method of claim 1 further comprising:pre-processing one of the data signals to detect a data qualitydeficiency; and identifying a cause of the data quality deficiency andcorrecting the data quality deficiency or, if it is not possible tocorrect the data quality deficiency, generating an indication of thedata quality deficiency.
 4. The method of claim 3 wherein the dataquality deficiency comprises one selected from the group consisting of:a malfunctioning sensor, electrical noise, a sensor data freezecondition, a loss or perturbation of downhole telemetry, a loss of acommunication link, and a change in scale or units of a data signalindicating a parameter value.
 5. The method of claim 1 furthercomprising automatically adjusting operation of the electricalsubmersible pump upon detecting the event.
 6. The method of claim 1wherein generating the indication further comprises generating anindication of a root cause of the event and providing a suggestedoperation to a user.
 7. The method of claim 6 wherein the suggestedoperation comprises a modification of one or more operating parametersof the electric submersible pump and the method further comprisesrefining a determination of the root cause based on the operation of theelectric submersible pump after the one or more operating parameters aremodified.
 8. The method of claim 1 further comprising: prompting a userfor input following a deviation of one of the parameters from thereference signal associated with that parameter; and based on a receiveduser input, updating the value of the reference signal or updating thevalue of the predetermined threshold.
 9. The method of claim 1 whereinthe parameters comprise one or more of the group consisting of: a drivefrequency of the electric submersible pump, a motor current of theelectric submersible pump, a discharge pressure of the electricsubmersible pump, an intake pressure of the electric submersible pump, amotor temperature of the electric submersible pump, an intaketemperature of the electric submersible pump, a well head pressure, andone or more current parameters of a gauge of the electric submersiblepump.
 10. A system for monitoring operation of an electric submersiblepump, comprising: a plurality of sensors configured to generate dataindicative of a plurality of observable parameters regarding operationof the electric submersible pumping system; a processor coupled to thesensors configured to: receive the data from the sensors; establish, forat least some of the plurality of parameters, an associated referencesignal; detect a deviation of one of the parameters from the referencesignal associated with that parameter; as a result of the deviationhaving a rate of change below a predetermined threshold, update thevalue of the reference signal to reflect the deviation; and as a resultof the deviation having a rate of change above the predeterminedthreshold, generate an indication of an event; wherein the indication ofthe event further depends on a type of the parameter and a direction ofthe deviation from the reference signal.
 11. The system of claim 10wherein the event comprises one type of a plurality of event types andwherein a set of parameters observed to detect an event of a first typediffers from a set of parameters observed to detect an event of a secondtype.
 12. The system of claim 10 wherein the processor is furtherconfigured to: pre-process one of the data signals to detect a dataquality deficiency; and identify a cause of the data quality deficiencyand correcting the data quality deficiency or, if it is not possible tocorrect the data quality deficiency, generate an indication of the dataquality deficiency.
 13. The system of claim 12 wherein the data qualitydeficiency comprises one selected from the group consisting of: amalfunctioning sensor, electrical noise, a sensor data freeze condition,a loss or perturbation of downhole telemetry, a loss of a communicationlink, and a change in scale or units of a data signal indicating aparameter value.
 14. The system of claim 10 wherein the processor isfurther configured to adjust operation of the electrical submersiblepump upon detecting the event.
 15. The system of claim 10 wherein theprocessor is further configured to generate an indication of a rootcause of the event and provide a suggested operation to a user.
 16. Thesystem of claim 10 wherein the processor is further configured to:prompt a user for input following a deviation of one of the parametersfrom the reference signal associated with that parameter; and based on areceived user input, update the value of the reference signal orupdating the value of the predetermined threshold.
 17. The system ofclaim 10 wherein the parameters comprise one or more of the groupconsisting of: a drive frequency of the electric submersible pump, amotor current of the electric submersible pump, a discharge pressure ofthe electric submersible pump, an intake pressure of the electricsubmersible pump, a motor temperature of the electric submersible pump,an intake temperature of the electric submersible pump, a well headpressure, and one or more current parameters of a gauge of the electricsubmersible pump.
 18. A non-transitory computer-readable mediumcontaining instructions that, when executed by a processor, cause theprocessor to: receive data indicative of a plurality of observableparameters from one or more sensors; establish, for at least some of theplurality of parameters, an associated reference signal; detect adeviation of one of the parameters from the reference signal associatedwith that parameter; as a result of the deviation having a rate ofchange below a predetermined threshold, update the value of thereference signal to reflect the deviation; and as a result of thedeviation having a rate of change above the predetermined threshold,generate an indication of an event; wherein the indication of the eventfurther depends on a type of the parameter and a direction of thedeviation from the reference signal.
 19. The non-transitorycomputer-readable medium of claim 18 wherein the instructions furthercause the processor to: pre-process one of the data signals to detect adata quality deficiency; and identify a cause of the data qualitydeficiency and correcting the data quality deficiency or, if it is notpossible to correct the data quality deficiency, generate an indicationof the data quality deficiency.
 20. The non-transitory computer-readablemedium of claim 18 wherein the instructions further cause the processorto adjust operation of the electrical submersible pump upon detectingthe event.
 21. The non-transitory computer-readable medium of claim 18wherein the instructions further cause the processor to generate anindication of a root cause of the event and provide a suggestedoperation to a user.
 22. The non-transitory computer-readable medium ofclaim 18 wherein the instructions further cause the processor to: prompta user for input following a deviation of one of the parameters from thereference signal associated with that parameter; and based on a receiveduser input, update the value of the reference signal or updating thevalue of the predetermined threshold.